{
 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "## 基于迁移学习的蚁蜂分类模型"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "---"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### 介绍"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "在训练深度学习模型时，有时我们没有海量的训练样本，只有少数的训练样本(比如几百个图片)，这显然对于深度学习远远不够。这时候，我们可以使用别人预训练好的网络模型权重，在此基础上进行训练，这就是迁移学习(Transfer Learning)。本实验将利用迁移学习的概念，训练一个蜜蜂和蚂蚁的分类模型。\n"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### 知识点"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "- 数据的预处理\n",
    "- 迁移学习\n",
    "- 预训练模型\n",
    "- 模型的训练与测试"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "---"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "###  数据的预处理 "
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "在建立模型之前，让我们还是先从蓝桥云课中下载所需要的数据集合。"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "source": [
    "!wget -nc \"https://labfile.oss.aliyuncs.com/courses/2534/hymenoptera_data.zip\"\r\n",
    "#!unzip hymenoptera_data.zip"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Active code page: 65001\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "--2021-08-14 14:31:13--  https://labfile.oss.aliyuncs.com/courses/2534/hymenoptera_data.zip\n",
      "Resolving labfile.oss.aliyuncs.com (labfile.oss.aliyuncs.com)... 47.110.177.159\n",
      "Connecting to labfile.oss.aliyuncs.com (labfile.oss.aliyuncs.com)|47.110.177.159|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 47286322 (45M) [application/zip]\n",
      "Saving to: 'hymenoptera_data.zip'\n",
      "\n",
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      " 10350K .......... .......... .......... .......... .......... 22% 13.0M 8s\n",
      " 10400K .......... .......... .......... .......... .......... 22% 7.82M 8s\n",
      " 10450K .......... .......... .......... .......... .......... 22% 5.25M 8s\n",
      " 10500K .......... .......... .......... .......... .......... 22% 7.17M 8s\n",
      " 10550K .......... .......... .......... .......... .......... 22% 15.0M 8s\n",
      " 10600K .......... .......... .......... .......... .......... 23% 3.19M 8s\n",
      " 10650K .......... .......... .......... .......... .......... 23% 8.59M 8s\n",
      " 10700K .......... .......... .......... .......... .......... 23% 30.4M 8s\n",
      " 10750K .......... .......... .......... .......... .......... 23% 1.75M 8s\n",
      " 10800K .......... .......... .......... .......... .......... 23% 11.8M 8s\n",
      " 10850K .......... .......... .......... .......... .......... 23% 14.3M 8s\n",
      " 10900K .......... .......... .......... .......... .......... 23% 5.59M 8s\n",
      " 10950K .......... .......... .......... .......... .......... 23% 8.75M 8s\n",
      " 11000K .......... .......... .......... .......... .......... 23% 10.9M 8s\n",
      " 11050K .......... .......... .......... .......... .......... 24% 5.98M 8s\n",
      " 11100K .......... .......... .......... .......... .......... 24% 13.6M 8s\n",
      " 11150K .......... .......... .......... .......... .......... 24% 12.0M 8s\n",
      " 11200K .......... .......... .......... .......... .......... 24% 6.35M 8s\n",
      " 11250K .......... .......... .......... .......... .......... 24% 14.4M 8s\n",
      " 11300K .......... .......... .......... .......... .......... 24% 7.27M 8s\n",
      " 11350K .......... .......... .......... .......... .......... 24% 14.9M 8s\n",
      " 11400K .......... .......... .......... .......... .......... 24% 13.9M 8s\n",
      " 11450K .......... .......... .......... .......... .......... 24% 4.02M 8s\n",
      " 11500K .......... .......... .......... .......... .......... 25%  765K 8s\n",
      " 11550K .......... .......... .......... .......... .......... 25% 3.47M 8s\n",
      " 11600K .......... .......... .......... .......... .......... 25% 2.65M 8s\n",
      " 11650K .......... .......... .......... .......... .......... 25% 1.88M 8s\n",
      " 11700K .......... .......... .......... .......... .......... 25% 13.0M 8s\n",
      " 11750K .......... .......... .......... .......... .......... 25% 4.56M 8s\n",
      " 11800K .......... .......... .......... .......... .......... 25% 22.3M 8s\n",
      " 11850K .......... .......... .......... .......... .......... 25% 4.84M 8s\n",
      " 11900K .......... .......... .......... .......... .......... 25% 8.08M 8s\n",
      " 11950K .......... .......... .......... .......... .......... 25% 15.1M 8s\n",
      " 12000K .......... .......... .......... .......... .......... 26% 4.29M 8s\n",
      " 12050K .......... .......... .......... .......... .......... 26% 4.12M 8s\n",
      " 12100K .......... .......... .......... .......... .......... 26% 1.53M 8s\n",
      " 12150K .......... .......... .......... .......... .......... 26% 6.05M 8s\n",
      " 12200K .......... .......... .......... .......... .......... 26% 8.99M 8s\n",
      " 12250K .......... .......... .......... .......... .......... 26% 25.9M 8s\n",
      " 12300K .......... .......... .......... .......... .......... 26% 1.68M 8s\n",
      " 12350K .......... .......... .......... .......... .......... 26%  110M 8s\n",
      " 12400K .......... .......... .......... .......... .......... 26% 94.2M 8s\n",
      " 12450K .......... .......... .......... .......... .......... 27% 93.6M 8s\n",
      " 12500K .......... .......... .......... .......... .......... 27% 9.09M 8s\n",
      " 12550K .......... .......... .......... .......... .......... 27% 96.5M 8s\n",
      " 12600K .......... .......... .......... .......... .......... 27% 13.7M 8s\n",
      " 12650K .......... .......... .......... .......... .......... 27% 10.1M 8s\n",
      " 12700K .......... .......... .......... .......... .......... 27% 12.4M 8s\n",
      " 12750K .......... .......... .......... .......... .......... 27% 10.6M 8s\n",
      " 12800K .......... .......... .......... .......... .......... 27% 7.33M 8s\n",
      " 12850K .......... .......... .......... .......... .......... 27% 14.3M 7s\n",
      " 12900K .......... .......... .......... .......... .......... 28% 15.4M 7s\n",
      " 12950K .......... .......... .......... .......... .......... 28% 6.86M 7s\n",
      " 13000K .......... .......... .......... .......... .......... 28% 21.5M 7s\n",
      " 13050K .......... .......... .......... .......... .......... 28% 15.2M 7s\n",
      " 13100K .......... .......... .......... .......... .......... 28% 9.05M 7s\n",
      " 13150K .......... .......... .......... .......... .......... 28% 24.8M 7s\n",
      " 13200K .......... .......... .......... .......... .......... 28% 2.77M 7s\n",
      " 13250K .......... .......... .......... .......... .......... 28% 15.0M 7s\n",
      " 13300K .......... .......... .......... .......... .......... 28% 6.12M 7s\n",
      " 13350K .......... .......... .......... .......... .......... 29% 24.8M 7s\n",
      " 13400K .......... .......... .......... .......... .......... 29% 3.16M 7s\n",
      " 13450K .......... .......... .......... .......... .......... 29% 15.2M 7s\n",
      " 13500K .......... .......... .......... .......... .......... 29% 11.0M 7s\n",
      " 13550K .......... .......... .......... .......... .......... 29% 21.9M 7s\n",
      " 13600K .......... .......... .......... .......... .......... 29% 12.1M 7s\n",
      " 13650K .......... .......... .......... .......... .......... 29% 12.3M 7s\n",
      " 13700K .......... .......... .......... .......... .......... 29% 5.59M 7s\n",
      " 13750K .......... .......... .......... .......... .......... 29% 6.60M 7s\n",
      " 13800K .......... .......... .......... .......... .......... 29% 4.29M 7s\n",
      " 13850K .......... .......... .......... .......... .......... 30% 10.1M 7s\n",
      " 13900K .......... .......... .......... .......... .......... 30% 22.1M 7s\n",
      " 13950K .......... .......... .......... .......... .......... 30% 4.98M 7s\n",
      " 14000K .......... .......... .......... .......... .......... 30% 5.28M 7s\n",
      " 14050K .......... .......... .......... .......... .......... 30% 9.43M 7s\n",
      " 14100K .......... .......... .......... .......... .......... 30% 6.43M 7s\n",
      " 14150K .......... .......... .......... .......... .......... 30% 13.0M 7s\n",
      " 14200K .......... .......... .......... .......... .......... 30% 7.01M 7s\n",
      " 14250K .......... .......... .......... .......... .......... 30% 5.25M 7s\n",
      " 14300K .......... .......... .......... .......... .......... 31% 21.2M 7s\n",
      " 14350K .......... .......... .......... .......... .......... 31% 6.21M 7s\n",
      " 14400K .......... .......... .......... .......... .......... 31% 7.11M 7s\n",
      " 14450K .......... .......... .......... .......... .......... 31% 9.76M 7s\n",
      " 14500K .......... .......... .......... .......... .......... 31% 13.4M 7s\n",
      " 14550K .......... .......... .......... .......... .......... 31% 6.19M 7s\n",
      " 14600K .......... .......... .......... .......... .......... 31% 14.4M 7s\n",
      " 14650K .......... .......... .......... .......... .......... 31% 11.6M 7s\n",
      " 14700K .......... .......... .......... .......... .......... 31% 22.1M 7s\n",
      " 14750K .......... .......... .......... .......... .......... 32% 8.62M 7s\n",
      " 14800K .......... .......... .......... .......... .......... 32% 11.4M 7s\n",
      " 14850K .......... .......... .......... .......... .......... 32% 13.8M 7s\n",
      " 14900K .......... .......... .......... .......... .......... 32% 12.3M 7s\n",
      " 14950K .......... .......... .......... .......... .......... 32% 11.8M 7s\n",
      " 15000K .......... .......... .......... .......... .......... 32% 2.78M 7s\n",
      " 15050K .......... .......... .......... .......... .......... 32% 5.35M 7s\n",
      " 15100K .......... .......... .......... .......... .......... 32% 9.43M 6s\n",
      " 15150K .......... .......... .......... .......... .......... 32% 3.01M 6s\n",
      " 15200K .......... .......... .......... .......... .......... 33% 9.23M 6s\n",
      " 15250K .......... .......... .......... .......... .......... 33% 12.0M 6s\n",
      " 15300K .......... .......... .......... .......... .......... 33% 11.5M 6s\n",
      " 15350K .......... .......... .......... .......... .......... 33% 4.53M 6s\n",
      " 15400K .......... .......... .......... .......... .......... 33% 5.10M 6s\n",
      " 15450K .......... .......... .......... .......... .......... 33% 5.28M 6s\n",
      " 15500K .......... .......... .......... .......... .......... 33% 16.3M 6s\n",
      " 15550K .......... .......... .......... .......... .......... 33% 4.50M 6s\n",
      " 15600K .......... .......... .......... .......... .......... 33% 6.48M 6s\n",
      " 15650K .......... .......... .......... .......... .......... 33% 5.74M 6s\n",
      " 15700K .......... .......... .......... .......... .......... 34% 3.24M 6s\n",
      " 15750K .......... .......... .......... .......... .......... 34% 9.30M 6s\n",
      " 15800K .......... .......... .......... .......... .......... 34% 17.7M 6s\n",
      " 15850K .......... .......... .......... .......... .......... 34% 4.33M 6s\n",
      " 15900K .......... .......... .......... .......... .......... 34% 3.11M 6s\n",
      " 15950K .......... .......... .......... .......... .......... 34% 22.6M 6s\n",
      " 16000K .......... .......... .......... .......... .......... 34% 8.34M 6s\n",
      " 16050K .......... .......... .......... .......... .......... 34% 9.54M 6s\n",
      " 16100K .......... .......... .......... .......... .......... 34% 6.65M 6s\n",
      " 16150K .......... .......... .......... .......... .......... 35% 10.6M 6s\n",
      " 16200K .......... .......... .......... .......... .......... 35% 10.5M 6s\n",
      " 16250K .......... .......... .......... .......... .......... 35% 9.89M 6s\n",
      " 16300K .......... .......... .......... .......... .......... 35% 5.08M 6s\n",
      " 16350K .......... .......... .......... .......... .......... 35% 2.11M 6s\n",
      " 16400K .......... .......... .......... .......... .......... 35% 22.6M 6s\n",
      " 16450K .......... .......... .......... .......... .......... 35% 8.84M 6s\n",
      " 16500K .......... .......... .......... .......... .......... 35% 17.5M 6s\n",
      " 16550K .......... .......... .......... .......... .......... 35% 4.88M 6s\n",
      " 16600K .......... .......... .......... .......... .......... 36% 19.8M 6s\n",
      " 16650K .......... .......... .......... .......... .......... 36% 3.45M 6s\n",
      " 16700K .......... .......... .......... .......... .......... 36% 16.5M 6s\n",
      " 16750K .......... .......... .......... .......... .......... 36% 11.0M 6s\n",
      " 16800K .......... .......... .......... .......... .......... 36% 8.54M 6s\n",
      " 16850K .......... .......... .......... .......... .......... 36% 6.93M 6s\n",
      " 16900K .......... .......... .......... .......... .......... 36% 3.38M 6s\n",
      " 16950K .......... .......... .......... .......... .......... 36% 65.5M 6s\n",
      " 17000K .......... .......... .......... .......... .......... 36% 8.12M 6s\n",
      " 17050K .......... .......... .......... .......... .......... 37% 4.50M 6s\n",
      " 17100K .......... .......... .......... .......... .......... 37% 12.7M 6s\n",
      " 17150K .......... .......... .......... .......... .......... 37% 5.36M 6s\n",
      " 17200K .......... .......... .......... .......... .......... 37% 7.35M 6s\n",
      " 17250K .......... .......... .......... .......... .......... 37% 7.71M 6s\n",
      " 17300K .......... .......... .......... .......... .......... 37% 13.2M 6s\n",
      " 17350K .......... .......... .......... .......... .......... 37% 11.1M 6s\n",
      " 17400K .......... .......... .......... .......... .......... 37% 3.72M 6s\n",
      " 17450K .......... .......... .......... .......... .......... 37% 3.16M 6s\n",
      " 17500K .......... .......... .......... .......... .......... 38% 84.5M 6s\n",
      " 17550K .......... .......... .......... .......... .......... 38%  125M 6s\n",
      " 17600K .......... .......... .......... .......... .......... 38% 3.50M 6s\n",
      " 17650K .......... .......... .......... .......... .......... 38%  113M 6s\n",
      " 17700K .......... .......... .......... .......... .......... 38%  105M 6s\n",
      " 17750K .......... .......... .......... .......... .......... 38%  113M 6s\n",
      " 17800K .......... .......... .......... .......... .......... 38% 9.97M 6s\n",
      " 17850K .......... .......... .......... .......... .......... 38% 68.9M 6s\n",
      " 17900K .......... .......... .......... .......... .......... 38% 11.1M 6s\n",
      " 17950K .......... .......... .......... .......... .......... 38% 6.57M 6s\n",
      " 18000K .......... .......... .......... .......... .......... 39% 7.47M 6s\n",
      " 18050K .......... .......... .......... .......... .......... 39% 15.0M 6s\n",
      " 18100K .......... .......... .......... .......... .......... 39% 11.0M 6s\n",
      " 18150K .......... .......... .......... .......... .......... 39% 3.79M 6s\n",
      " 18200K .......... .......... .......... .......... .......... 39% 81.3M 5s\n",
      " 18250K .......... .......... .......... .......... .......... 39% 66.4M 5s\n",
      " 18300K .......... .......... .......... .......... .......... 39% 8.31M 5s\n",
      " 18350K .......... .......... .......... .......... .......... 39% 11.8M 5s\n",
      " 18400K .......... .......... .......... .......... .......... 39% 34.3M 5s\n",
      " 18450K .......... .......... .......... .......... .......... 40% 1.33M 5s\n",
      " 18500K .......... .......... .......... .......... .......... 40% 22.9M 5s\n",
      " 18550K .......... .......... .......... .......... .......... 40% 16.1M 5s\n",
      " 18600K .......... .......... .......... .......... .......... 40% 7.80M 5s\n",
      " 18650K .......... .......... .......... .......... .......... 40% 41.4M 5s\n",
      " 18700K .......... .......... .......... .......... .......... 40% 2.24M 5s\n",
      " 18750K .......... .......... .......... .......... .......... 40% 15.2M 5s\n",
      " 18800K .......... .......... .......... .......... .......... 40% 2.45M 5s\n",
      " 18850K .......... .......... .......... .......... .......... 40% 9.22M 5s\n",
      " 18900K .......... .......... .......... .......... .......... 41% 17.0M 5s\n",
      " 18950K .......... .......... .......... .......... .......... 41% 9.75M 5s\n",
      " 19000K .......... .......... .......... .......... .......... 41% 22.4M 5s\n",
      " 19050K .......... .......... .......... .......... .......... 41% 12.5M 5s\n",
      " 19100K .......... .......... .......... .......... .......... 41% 12.2M 5s\n",
      " 19150K .......... .......... .......... .......... .......... 41% 15.3M 5s\n",
      " 19200K .......... .......... .......... .......... .......... 41% 12.1M 5s\n",
      " 19250K .......... .......... .......... .......... .......... 41% 10.2M 5s\n",
      " 19300K .......... .......... .......... .......... .......... 41% 23.9M 5s\n",
      " 19350K .......... .......... .......... .......... .......... 42% 14.2M 5s\n",
      " 19400K .......... .......... .......... .......... .......... 42% 3.48M 5s\n",
      " 19450K .......... .......... .......... .......... .......... 42% 10.4M 5s\n",
      " 19500K .......... .......... .......... .......... .......... 42% 7.62M 5s\n",
      " 19550K .......... .......... .......... .......... .......... 42% 3.21M 5s\n",
      " 19600K .......... .......... .......... .......... .......... 42% 8.36M 5s\n",
      " 19650K .......... .......... .......... .......... .......... 42% 8.61M 5s\n",
      " 19700K .......... .......... .......... .......... .......... 42% 11.2M 5s\n",
      " 19750K .......... .......... .......... .......... .......... 42% 14.9M 5s\n",
      " 19800K .......... .......... .......... .......... .......... 42% 4.69M 5s\n",
      " 19850K .......... .......... .......... .......... .......... 43% 12.4M 5s\n",
      " 19900K .......... .......... .......... .......... .......... 43% 11.7M 5s\n",
      " 19950K .......... .......... .......... .......... .......... 43% 6.87M 5s\n",
      " 20000K .......... .......... .......... .......... .......... 43% 21.9M 5s\n",
      " 20050K .......... .......... .......... .......... .......... 43% 9.87M 5s\n",
      " 20100K .......... .......... .......... .......... .......... 43% 7.79M 5s\n",
      " 20150K .......... .......... .......... .......... .......... 43% 4.41M 5s\n",
      " 20200K .......... .......... .......... .......... .......... 43% 14.5M 5s\n",
      " 20250K .......... .......... .......... .......... .......... 43% 7.10M 5s\n",
      " 20300K .......... .......... .......... .......... .......... 44% 11.7M 5s\n",
      " 20350K .......... .......... .......... .......... .......... 44% 15.2M 5s\n",
      " 20400K .......... .......... .......... .......... .......... 44% 1.30M 5s\n",
      " 20450K .......... .......... .......... .......... .......... 44% 1.94M 5s\n",
      " 20500K .......... .......... .......... .......... .......... 44% 13.3M 5s\n",
      " 20550K .......... .......... .......... .......... .......... 44% 14.1M 5s\n",
      " 20600K .......... .......... .......... .......... .......... 44% 12.5M 5s\n",
      " 20650K .......... .......... .......... .......... .......... 44% 6.00M 5s\n",
      " 20700K .......... .......... .......... .......... .......... 44% 98.4M 5s\n",
      " 20750K .......... .......... .......... .......... .......... 45% 15.7M 5s\n",
      " 20800K .......... .......... .......... .......... .......... 45% 12.9M 5s\n",
      " 20850K .......... .......... .......... .......... .......... 45% 6.39M 5s\n",
      " 20900K .......... .......... .......... .......... .......... 45% 16.4M 5s\n",
      " 20950K .......... .......... .......... .......... .......... 45% 10.5M 5s\n",
      " 21000K .......... .......... .......... .......... .......... 45% 21.2M 5s\n",
      " 21050K .......... .......... .......... .......... .......... 45% 4.69M 5s\n",
      " 21100K .......... .......... .......... .......... .......... 45% 12.3M 5s\n",
      " 21150K .......... .......... .......... .......... .......... 45% 8.95M 5s\n",
      " 21200K .......... .......... .......... .......... .......... 46% 6.21M 5s\n",
      " 21250K .......... .......... .......... .......... .......... 46% 2.05M 5s\n",
      " 21300K .......... .......... .......... .......... .......... 46% 22.3M 5s\n",
      " 21350K .......... .......... .......... .......... .......... 46% 6.83M 5s\n",
      " 21400K .......... .......... .......... .......... .......... 46% 6.24M 5s\n",
      " 21450K .......... .......... .......... .......... .......... 46% 12.5M 5s\n",
      " 21500K .......... .......... .......... .......... .......... 46% 16.1M 5s\n",
      " 21550K .......... .......... .......... .......... .......... 46% 20.7M 5s\n",
      " 21600K .......... .......... .......... .......... .......... 46% 2.70M 5s\n",
      " 21650K .......... .......... .......... .......... .......... 46% 15.2M 5s\n",
      " 21700K .......... .......... .......... .......... .......... 47% 5.64M 5s\n",
      " 21750K .......... .......... .......... .......... .......... 47% 21.3M 5s\n",
      " 21800K .......... .......... .......... .......... .......... 47% 4.20M 5s\n",
      " 21850K .......... .......... .......... .......... .......... 47% 3.30M 5s\n",
      " 21900K .......... .......... .......... .......... .......... 47% 10.5M 5s\n",
      " 21950K .......... .......... .......... .......... .......... 47% 8.17M 5s\n",
      " 22000K .......... .......... .......... .......... .......... 47% 9.86M 5s\n",
      " 22050K .......... .......... .......... .......... .......... 47% 8.03M 4s\n",
      " 22100K .......... .......... .......... .......... .......... 47% 2.83M 4s\n",
      " 22150K .......... .......... .......... .......... .......... 48% 4.01M 4s\n",
      " 22200K .......... .......... .......... .......... .......... 48% 20.2M 4s\n",
      " 22250K .......... .......... .......... .......... .......... 48% 4.18M 4s\n",
      " 22300K .......... .......... .......... .......... .......... 48% 2.66M 4s\n",
      " 22350K .......... .......... .......... .......... .......... 48% 5.14M 4s\n",
      " 22400K .......... .......... .......... .......... .......... 48% 6.15M 4s\n",
      " 22450K .......... .......... .......... .......... .......... 48% 13.0M 4s\n",
      " 22500K .......... .......... .......... .......... .......... 48% 5.07M 4s\n",
      " 22550K .......... .......... .......... .......... .......... 48% 12.6M 4s\n",
      " 22600K .......... .......... .......... .......... .......... 49% 14.1M 4s\n",
      " 22650K .......... .......... .......... .......... .......... 49% 6.81M 4s\n",
      " 22700K .......... .......... .......... .......... .......... 49% 1.96M 4s\n",
      " 22750K .......... .......... .......... .......... .......... 49% 25.3M 4s\n",
      " 22800K .......... .......... .......... .......... .......... 49% 8.06M 4s\n",
      " 22850K .......... .......... .......... .......... .......... 49% 11.0M 4s\n",
      " 22900K .......... .......... .......... .......... .......... 49% 4.28M 4s\n",
      " 22950K .......... .......... .......... .......... .......... 49% 21.8M 4s\n",
      " 23000K .......... .......... .......... .......... .......... 49% 4.72M 4s\n",
      " 23050K .......... .......... .......... .......... .......... 50% 15.3M 4s\n",
      " 23100K .......... .......... .......... .......... .......... 50% 2.42M 4s\n",
      " 23150K .......... .......... .......... .......... .......... 50% 1.73M 4s\n",
      " 23200K .......... .......... .......... .......... .......... 50% 13.9M 4s\n",
      " 23250K .......... .......... .......... .......... .......... 50% 2.80M 4s\n",
      " 23300K .......... .......... .......... .......... .......... 50%  979K 4s\n",
      " 23350K .......... .......... .......... .......... .......... 50% 11.3M 4s\n",
      " 23400K .......... .......... .......... .......... .......... 50% 8.33M 4s\n",
      " 23450K .......... .......... .......... .......... .......... 50% 4.77M 4s\n",
      " 23500K .......... .......... .......... .......... .......... 50% 12.1M 4s\n",
      " 23550K .......... .......... .......... .......... .......... 51% 5.66M 4s\n",
      " 23600K .......... .......... .......... .......... .......... 51% 12.1M 4s\n",
      " 23650K .......... .......... .......... .......... .......... 51%  951K 4s\n",
      " 23700K .......... .......... .......... .......... .......... 51% 12.3M 4s\n",
      " 23750K .......... .......... .......... .......... .......... 51% 5.38M 4s\n",
      " 23800K .......... .......... .......... .......... .......... 51% 11.6M 4s\n",
      " 23850K .......... .......... .......... .......... .......... 51% 13.8M 4s\n",
      " 23900K .......... .......... .......... .......... .......... 51% 10.7M 4s\n",
      " 23950K .......... .......... .......... .......... .......... 51% 16.3M 4s\n",
      " 24000K .......... .......... .......... .......... .......... 52% 22.4M 4s\n",
      " 24050K .......... .......... .......... .......... .......... 52% 15.1M 4s\n",
      " 24100K .......... .......... .......... .......... .......... 52% 8.82M 4s\n",
      " 24150K .......... .......... .......... .......... .......... 52% 9.41M 4s\n",
      " 24200K .......... .......... .......... .......... .......... 52% 8.36M 4s\n",
      " 24250K .......... .......... .......... .......... .......... 52% 7.93M 4s\n",
      " 24300K .......... .......... .......... .......... .......... 52% 2.15M 4s\n",
      " 24350K .......... .......... .......... .......... .......... 52% 11.4M 4s\n",
      " 24400K .......... .......... .......... .......... .......... 52% 7.49M 4s\n",
      " 24450K .......... .......... .......... .......... .......... 53% 3.82M 4s\n",
      " 24500K .......... .......... .......... .......... .......... 53% 12.0M 4s\n",
      " 24550K .......... .......... .......... .......... .......... 53% 10.1M 4s\n",
      " 24600K .......... .......... .......... .......... .......... 53% 12.2M 4s\n",
      " 24650K .......... .......... .......... .......... .......... 53% 14.1M 4s\n",
      " 24700K .......... .......... .......... .......... .......... 53% 12.5M 4s\n",
      " 24750K .......... .......... .......... .......... .......... 53% 3.67M 4s\n",
      " 24800K .......... .......... .......... .......... .......... 53% 15.2M 4s\n",
      " 24850K .......... .......... .......... .......... .......... 53% 8.59M 4s\n",
      " 24900K .......... .......... .......... .......... .......... 54% 15.2M 4s\n",
      " 24950K .......... .......... .......... .......... .......... 54% 9.73M 4s\n",
      " 25000K .......... .......... .......... .......... .......... 54% 22.7M 4s\n",
      " 25050K .......... .......... .......... .......... .......... 54% 5.99M 4s\n",
      " 25100K .......... .......... .......... .......... .......... 54% 19.9M 4s\n",
      " 25150K .......... .......... .......... .......... .......... 54% 16.5M 4s\n",
      " 25200K .......... .......... .......... .......... .......... 54% 11.2M 4s\n",
      " 25250K .......... .......... .......... .......... .......... 54% 5.13M 4s\n",
      " 25300K .......... .......... .......... .......... .......... 54% 19.5M 4s\n",
      " 25350K .......... .......... .......... .......... .......... 55% 4.26M 4s\n",
      " 25400K .......... .......... .......... .......... .......... 55% 6.83M 4s\n",
      " 25450K .......... .......... .......... .......... .......... 55% 8.48M 4s\n",
      " 25500K .......... .......... .......... .......... .......... 55% 7.48M 4s\n",
      " 25550K .......... .......... .......... .......... .......... 55% 8.24M 4s\n",
      " 25600K .......... .......... .......... .......... .......... 55% 22.6M 4s\n",
      " 25650K .......... .......... .......... .......... .......... 55% 8.52M 4s\n",
      " 25700K .......... .......... .......... .......... .......... 55% 22.5M 4s\n",
      " 25750K .......... .......... .......... .......... .......... 55% 4.83M 4s\n",
      " 25800K .......... .......... .......... .......... .......... 55% 9.87M 4s\n",
      " 25850K .......... .......... .......... .......... .......... 56% 12.5M 4s\n",
      " 25900K .......... .......... .......... .......... .......... 56% 1.29M 4s\n",
      " 25950K .......... .......... .......... .......... .......... 56% 5.18M 4s\n",
      " 26000K .......... .......... .......... .......... .......... 56% 12.0M 4s\n",
      " 26050K .......... .......... .......... .......... .......... 56% 12.9M 4s\n",
      " 26100K .......... .......... .......... .......... .......... 56% 12.8M 4s\n",
      " 26150K .......... .......... .......... .......... .......... 56% 6.16M 4s\n",
      " 26200K .......... .......... .......... .......... .......... 56% 14.5M 4s\n",
      " 26250K .......... .......... .......... .......... .......... 56% 12.7M 4s\n",
      " 26300K .......... .......... .......... .......... .......... 57% 8.57M 4s\n",
      " 26350K .......... .......... .......... .......... .......... 57% 13.1M 4s\n",
      " 26400K .......... .......... .......... .......... .......... 57% 3.43M 4s\n",
      " 26450K .......... .......... .......... .......... .......... 57% 23.4M 4s\n",
      " 26500K .......... .......... .......... .......... .......... 57% 16.5M 4s\n",
      " 26550K .......... .......... .......... .......... .......... 57% 7.12M 4s\n",
      " 26600K .......... .......... .......... .......... .......... 57% 17.0M 4s\n",
      " 26650K .......... .......... .......... .......... .......... 57% 2.50M 4s\n",
      " 26700K .......... .......... .......... .......... .......... 57% 22.9M 4s\n",
      " 26750K .......... .......... .......... .......... .......... 58% 15.8M 4s\n",
      " 26800K .......... .......... .......... .......... .......... 58% 9.89M 4s\n",
      " 26850K .......... .......... .......... .......... .......... 58% 19.9M 4s\n",
      " 26900K .......... .......... .......... .......... .......... 58%  861K 4s\n",
      " 26950K .......... .......... .......... .......... .......... 58% 12.5M 4s\n",
      " 27000K .......... .......... .......... .......... .......... 58% 13.6M 4s\n",
      " 27050K .......... .......... .......... .......... .......... 58% 12.4M 4s\n",
      " 27100K .......... .......... .......... .......... .......... 58% 13.9M 3s\n",
      " 27150K .......... .......... .......... .......... .......... 58% 9.99M 3s\n",
      " 27200K .......... .......... .......... .......... .......... 59% 12.5M 3s\n",
      " 27250K .......... .......... .......... .......... .......... 59% 7.83M 3s\n",
      " 27300K .......... .......... .......... .......... .......... 59% 11.9M 3s\n",
      " 27350K .......... .......... .......... .......... .......... 59% 8.88M 3s\n",
      " 27400K .......... .......... .......... .......... .......... 59% 9.14M 3s\n",
      " 27450K .......... .......... .......... .......... .......... 59% 5.69M 3s\n",
      " 27500K .......... .......... .......... .......... .......... 59% 4.17M 3s\n",
      " 27550K .......... .......... .......... .......... .......... 59% 12.3M 3s\n",
      " 27600K .......... .......... .......... .......... .......... 59% 4.24M 3s\n",
      " 27650K .......... .......... .......... .......... .......... 59% 11.3M 3s\n",
      " 27700K .......... .......... .......... .......... .......... 60% 11.8M 3s\n",
      " 27750K .......... .......... .......... .......... .......... 60% 3.63M 3s\n",
      " 27800K .......... .......... .......... .......... .......... 60% 23.2M 3s\n",
      " 27850K .......... .......... .......... .......... .......... 60% 15.0M 3s\n",
      " 27900K .......... .......... .......... .......... .......... 60% 6.78M 3s\n",
      " 27950K .......... .......... .......... .......... .......... 60% 14.2M 3s\n",
      " 28000K .......... .......... .......... .......... .......... 60% 11.7M 3s\n",
      " 28050K .......... .......... .......... .......... .......... 60% 21.0M 3s\n",
      " 28100K .......... .......... .......... .......... .......... 60% 14.6M 3s\n",
      " 28150K .......... .......... .......... .......... .......... 61% 5.65M 3s\n",
      " 28200K .......... .......... .......... .......... .......... 61% 14.5M 3s\n",
      " 28250K .......... .......... .......... .......... .......... 61% 9.28M 3s\n",
      " 28300K .......... .......... .......... .......... .......... 61% 7.74M 3s\n",
      " 28350K .......... .......... .......... .......... .......... 61% 12.2M 3s\n",
      " 28400K .......... .......... .......... .......... .......... 61% 12.7M 3s\n",
      " 28450K .......... .......... .......... .......... .......... 61% 11.1M 3s\n",
      " 28500K .......... .......... .......... .......... .......... 61% 9.32M 3s\n",
      " 28550K .......... .......... .......... .......... .......... 61% 23.4M 3s\n",
      " 28600K .......... .......... .......... .......... .......... 62% 10.8M 3s\n",
      " 28650K .......... .......... .......... .......... .......... 62% 13.1M 3s\n",
      " 28700K .......... .......... .......... .......... .......... 62% 12.3M 3s\n",
      " 28750K .......... .......... .......... .......... .......... 62% 12.9M 3s\n",
      " 28800K .......... .......... .......... .......... .......... 62% 11.6M 3s\n",
      " 28850K .......... .......... .......... .......... .......... 62% 7.74M 3s\n",
      " 28900K .......... .......... .......... .......... .......... 62% 16.0M 3s\n",
      " 28950K .......... .......... .......... .......... .......... 62% 10.4M 3s\n",
      " 29000K .......... .......... .......... .......... .......... 62% 20.1M 3s\n",
      " 29050K .......... .......... .......... .......... .......... 63% 14.7M 3s\n",
      " 29100K .......... .......... .......... .......... .......... 63% 12.3M 3s\n",
      " 29150K .......... .......... .......... .......... .......... 63% 6.06M 3s\n",
      " 29200K .......... .......... .......... .......... .......... 63% 21.8M 3s\n",
      " 29250K .......... .......... .......... .......... .......... 63% 1.73M 3s\n",
      " 29300K .......... .......... .......... .......... .......... 63% 4.85M 3s\n",
      " 29350K .......... .......... .......... .......... .......... 63% 9.16M 3s\n",
      " 29400K .......... .......... .......... .......... .......... 63% 6.98M 3s\n",
      " 29450K .......... .......... .......... .......... .......... 63% 3.16M 3s\n",
      " 29500K .......... .......... .......... .......... .......... 63% 8.16M 3s\n",
      " 29550K .......... .......... .......... .......... .......... 64% 5.13M 3s\n",
      " 29600K .......... .......... .......... .......... .......... 64% 2.64M 3s\n",
      " 29650K .......... .......... .......... .......... .......... 64% 15.6M 3s\n",
      " 29700K .......... .......... .......... .......... .......... 64% 4.21M 3s\n",
      " 29750K .......... .......... .......... .......... .......... 64% 4.70M 3s\n",
      " 29800K .......... .......... .......... .......... .......... 64% 1.53M 3s\n",
      " 29850K .......... .......... .......... .......... .......... 64% 8.15M 3s\n",
      " 29900K .......... .......... .......... .......... .......... 64%  904K 3s\n",
      " 29950K .......... .......... .......... .......... .......... 64% 21.7M 3s\n",
      " 30000K .......... .......... .......... .......... .......... 65% 16.4M 3s\n",
      " 30050K .......... .......... .......... .......... .......... 65% 10.8M 3s\n",
      " 30100K .......... .......... .......... .......... .......... 65% 9.13M 3s\n",
      " 30150K .......... .......... .......... .......... .......... 65% 11.4M 3s\n",
      " 30200K .......... .......... .......... .......... .......... 65% 9.32M 3s\n",
      " 30250K .......... .......... .......... .......... .......... 65% 24.0M 3s\n",
      " 30300K .......... .......... .......... .......... .......... 65% 4.26M 3s\n",
      " 30350K .......... .......... .......... .......... .......... 65% 21.3M 3s\n",
      " 30400K .......... .......... .......... .......... .......... 65% 12.2M 3s\n",
      " 30450K .......... .......... .......... .......... .......... 66% 4.57M 3s\n",
      " 30500K .......... .......... .......... .......... .......... 66% 21.3M 3s\n",
      " 30550K .......... .......... .......... .......... .......... 66% 3.94M 3s\n",
      " 30600K .......... .......... .......... .......... .......... 66% 9.35M 3s\n",
      " 30650K .......... .......... .......... .......... .......... 66% 4.20M 3s\n",
      " 30700K .......... .......... .......... .......... .......... 66% 7.63M 3s\n",
      " 30750K .......... .......... .......... .......... .......... 66% 11.0M 3s\n",
      " 30800K .......... .......... .......... .......... .......... 66% 7.11M 3s\n",
      " 30850K .......... .......... .......... .......... .......... 66% 7.08M 3s\n",
      " 30900K .......... .......... .......... .......... .......... 67% 8.15M 3s\n",
      " 30950K .......... .......... .......... .......... .......... 67% 1.40M 3s\n",
      " 31000K .......... .......... .......... .......... .......... 67% 15.7M 3s\n",
      " 31050K .......... .......... .......... .......... .......... 67% 9.93M 3s\n",
      " 31100K .......... .......... .......... .......... .......... 67% 8.53M 3s\n",
      " 31150K .......... .......... .......... .......... .......... 67% 3.06M 3s\n",
      " 31200K .......... .......... .......... .......... .......... 67% 87.7M 3s\n",
      " 31250K .......... .......... .......... .......... .......... 67% 5.63M 3s\n",
      " 31300K .......... .......... .......... .......... .......... 67% 12.9M 3s\n",
      " 31350K .......... .......... .......... .......... .......... 67% 6.54M 3s\n",
      " 31400K .......... .......... .......... .......... .......... 68% 21.5M 3s\n",
      " 31450K .......... .......... .......... .......... .......... 68% 14.7M 3s\n",
      " 31500K .......... .......... .......... .......... .......... 68% 12.1M 3s\n",
      " 31550K .......... .......... .......... .......... .......... 68% 16.4M 3s\n",
      " 31600K .......... .......... .......... .......... .......... 68% 7.36M 3s\n",
      " 31650K .......... .......... .......... .......... .......... 68% 14.4M 3s\n",
      " 31700K .......... .......... .......... .......... .......... 68% 8.85M 3s\n",
      " 31750K .......... .......... .......... .......... .......... 68% 11.6M 3s\n",
      " 31800K .......... .......... .......... .......... .......... 68% 23.8M 3s\n",
      " 31850K .......... .......... .......... .......... .......... 69% 11.5M 3s\n",
      " 31900K .......... .......... .......... .......... .......... 69% 12.5M 3s\n",
      " 31950K .......... .......... .......... .......... .......... 69% 7.74M 3s\n",
      " 32000K .......... .......... .......... .......... .......... 69% 7.75M 2s\n",
      " 32050K .......... .......... .......... .......... .......... 69% 21.6M 2s\n",
      " 32100K .......... .......... .......... .......... .......... 69% 11.4M 2s\n",
      " 32150K .......... .......... .......... .......... .......... 69% 5.53M 2s\n",
      " 32200K .......... .......... .......... .......... .......... 69% 2.26M 2s\n",
      " 32250K .......... .......... .......... .......... .......... 69% 22.6M 2s\n",
      " 32300K .......... .......... .......... .......... .......... 70% 5.55M 2s\n",
      " 32350K .......... .......... .......... .......... .......... 70% 11.9M 2s\n",
      " 32400K .......... .......... .......... .......... .......... 70% 6.18M 2s\n",
      " 32450K .......... .......... .......... .......... .......... 70% 7.42M 2s\n",
      " 32500K .......... .......... .......... .......... .......... 70% 11.1M 2s\n",
      " 32550K .......... .......... .......... .......... .......... 70% 12.0M 2s\n",
      " 32600K .......... .......... .......... .......... .......... 70% 1.14M 2s\n",
      " 32650K .......... .......... .......... .......... .......... 70% 8.58M 2s\n",
      " 32700K .......... .......... .......... .......... .......... 70% 9.47M 2s\n",
      " 32750K .......... .......... .......... .......... .......... 71% 4.55M 2s\n",
      " 32800K .......... .......... .......... .......... .......... 71% 6.65M 2s\n",
      " 32850K .......... .......... .......... .......... .......... 71% 7.37M 2s\n",
      " 32900K .......... .......... .......... .......... .......... 71% 12.2M 2s\n",
      " 32950K .......... .......... .......... .......... .......... 71% 10.5M 2s\n",
      " 33000K .......... .......... .......... .......... .......... 71% 4.20M 2s\n",
      " 33050K .......... .......... .......... .......... .......... 71% 11.8M 2s\n",
      " 33100K .......... .......... .......... .......... .......... 71% 5.52M 2s\n",
      " 33150K .......... .......... .......... .......... .......... 71% 8.18M 2s\n",
      " 33200K .......... .......... .......... .......... .......... 72% 11.3M 2s\n",
      " 33250K .......... .......... .......... .......... .......... 72% 9.54M 2s\n",
      " 33300K .......... .......... .......... .......... .......... 72% 8.07M 2s\n",
      " 33350K .......... .......... .......... .......... .......... 72% 12.0M 2s\n",
      " 33400K .......... .......... .......... .......... .......... 72% 13.1M 2s\n",
      " 33450K .......... .......... .......... .......... .......... 72% 9.16M 2s\n",
      " 33500K .......... .......... .......... .......... .......... 72% 8.49M 2s\n",
      " 33550K .......... .......... .......... .......... .......... 72% 7.18M 2s\n",
      " 33600K .......... .......... .......... .......... .......... 72% 1.15M 2s\n",
      " 33650K .......... .......... .......... .......... .......... 72% 8.53M 2s\n",
      " 33700K .......... .......... .......... .......... .......... 73% 7.22M 2s\n",
      " 33750K .......... .......... .......... .......... .......... 73% 9.39M 2s\n",
      " 33800K .......... .......... .......... .......... .......... 73% 10.4M 2s\n",
      " 33850K .......... .......... .......... .......... .......... 73% 7.84M 2s\n",
      " 33900K .......... .......... .......... .......... .......... 73% 4.29M 2s\n",
      " 33950K .......... .......... .......... .......... .......... 73% 3.08M 2s\n",
      " 34000K .......... .......... .......... .......... .......... 73% 1.21M 2s\n",
      " 34050K .......... .......... .......... .......... .......... 73% 13.6M 2s\n",
      " 34100K .......... .......... .......... .......... .......... 73% 3.55M 2s\n",
      " 34150K .......... .......... .......... .......... .......... 74% 11.6M 2s\n",
      " 34200K .......... .......... .......... .......... .......... 74% 10.8M 2s\n",
      " 34250K .......... .......... .......... .......... .......... 74% 12.5M 2s\n",
      " 34300K .......... .......... .......... .......... .......... 74% 5.13M 2s\n",
      " 34350K .......... .......... .......... .......... .......... 74%  488K 2s\n",
      " 34400K .......... .......... .......... .......... .......... 74% 3.55M 2s\n",
      " 34450K .......... .......... .......... .......... .......... 74% 93.9M 2s\n",
      " 34500K .......... .......... .......... .......... .......... 74%  121M 2s\n",
      " 34550K .......... .......... .......... .......... .......... 74% 12.3M 2s\n",
      " 34600K .......... .......... .......... .......... .......... 75% 12.2M 2s\n",
      " 34650K .......... .......... .......... .......... .......... 75% 3.22M 2s\n",
      " 34700K .......... .......... .......... .......... .......... 75% 4.19M 2s\n",
      " 34750K .......... .......... .......... .......... .......... 75% 12.3M 2s\n",
      " 34800K .......... .......... .......... .......... .......... 75% 12.2M 2s\n",
      " 34850K .......... .......... .......... .......... .......... 75% 5.17M 2s\n",
      " 34900K .......... .......... .......... .......... .......... 75% 8.01M 2s\n",
      " 34950K .......... .......... .......... .......... .......... 75% 7.22M 2s\n",
      " 35000K .......... .......... .......... .......... .......... 75% 3.26M 2s\n",
      " 35050K .......... .......... .......... .......... .......... 76% 13.0M 2s\n",
      " 35100K .......... .......... .......... .......... .......... 76% 7.77M 2s\n",
      " 35150K .......... .......... .......... .......... .......... 76% 13.9M 2s\n",
      " 35200K .......... .......... .......... .......... .......... 76% 7.66M 2s\n",
      " 35250K .......... .......... .......... .......... .......... 76% 12.4M 2s\n",
      " 35300K .......... .......... .......... .......... .......... 76% 11.7M 2s\n",
      " 35350K .......... .......... .......... .......... .......... 76% 3.18M 2s\n",
      " 35400K .......... .......... .......... .......... .......... 76% 7.12M 2s\n",
      " 35450K .......... .......... .......... .......... .......... 76% 11.2M 2s\n",
      " 35500K .......... .......... .......... .......... .......... 76% 14.2M 2s\n",
      " 35550K .......... .......... .......... .......... .......... 77% 11.2M 2s\n",
      " 35600K .......... .......... .......... .......... .......... 77% 3.96M 2s\n",
      " 35650K .......... .......... .......... .......... .......... 77% 7.33M 2s\n",
      " 35700K .......... .......... .......... .......... .......... 77% 3.22M 2s\n",
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      " 35800K .......... .......... .......... .......... .......... 77% 3.72M 2s\n",
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      " 35900K .......... .......... .......... .......... .......... 77% 10.6M 2s\n",
      " 35950K .......... .......... .......... .......... .......... 77% 7.74M 2s\n",
      " 36000K .......... .......... .......... .......... .......... 78% 8.79M 2s\n",
      " 36050K .......... .......... .......... .......... .......... 78% 3.91M 2s\n",
      " 36100K .......... .......... .......... .......... .......... 78% 7.17M 2s\n",
      " 36150K .......... .......... .......... .......... .......... 78% 6.30M 2s\n",
      " 36200K .......... .......... .......... .......... .......... 78% 76.0M 2s\n",
      " 36250K .......... .......... .......... .......... .......... 78% 5.68M 2s\n",
      " 36300K .......... .......... .......... .......... .......... 78% 12.8M 2s\n",
      " 36350K .......... .......... .......... .......... .......... 78% 13.6M 2s\n",
      " 36400K .......... .......... .......... .......... .......... 78% 11.4M 2s\n",
      " 36450K .......... .......... .......... .......... .......... 79% 1.73M 2s\n",
      " 36500K .......... .......... .......... .......... .......... 79% 9.47M 2s\n",
      " 36550K .......... .......... .......... .......... .......... 79% 20.7M 2s\n",
      " 36600K .......... .......... .......... .......... .......... 79% 12.1M 2s\n",
      " 36650K .......... .......... .......... .......... .......... 79% 13.7M 2s\n",
      " 36700K .......... .......... .......... .......... .......... 79% 5.10M 2s\n",
      " 36750K .......... .......... .......... .......... .......... 79% 8.70M 2s\n",
      " 36800K .......... .......... .......... .......... .......... 79% 12.5M 2s\n",
      " 36850K .......... .......... .......... .......... .......... 79% 12.1M 2s\n",
      " 36900K .......... .......... .......... .......... .......... 80% 7.10M 2s\n",
      " 36950K .......... .......... .......... .......... .......... 80% 12.1M 2s\n",
      " 37000K .......... .......... .......... .......... .......... 80% 1.04M 2s\n",
      " 37050K .......... .......... .......... .......... .......... 80% 12.8M 2s\n",
      " 37100K .......... .......... .......... .......... .......... 80% 6.45M 2s\n",
      " 37150K .......... .......... .......... .......... .......... 80% 22.2M 2s\n",
      " 37200K .......... .......... .......... .......... .......... 80% 1.06M 2s\n",
      " 37250K .......... .......... .......... .......... .......... 80% 22.5M 2s\n",
      " 37300K .......... .......... .......... .......... .......... 80% 3.47M 2s\n",
      " 37350K .......... .......... .......... .......... .......... 80% 23.4M 2s\n",
      " 37400K .......... .......... .......... .......... .......... 81% 17.4M 2s\n",
      " 37450K .......... .......... .......... .......... .......... 81% 8.08M 2s\n",
      " 37500K .......... .......... .......... .......... .......... 81% 9.46M 2s\n",
      " 37550K .......... .......... .......... .......... .......... 81% 22.9M 2s\n",
      " 37600K .......... .......... .......... .......... .......... 81% 9.24M 2s\n",
      " 37650K .......... .......... .......... .......... .......... 81% 21.6M 2s\n",
      " 37700K .......... .......... .......... .......... .......... 81% 23.2M 1s\n",
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      " 37800K .......... .......... .......... .......... .......... 81% 11.3M 1s\n",
      " 37850K .......... .......... .......... .......... .......... 82% 12.2M 1s\n",
      " 37900K .......... .......... .......... .......... .......... 82% 13.4M 1s\n",
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      " 45500K .......... .......... .......... .......... .......... 98% 7.82M 0s\n",
      " 45550K .......... .......... .......... .......... .......... 98% 2.30M 0s\n",
      " 45600K .......... .......... .......... .......... .......... 98% 3.38M 0s\n",
      " 45650K .......... .......... .......... .......... .......... 98% 22.2M 0s\n",
      " 45700K .......... .......... .......... .......... .......... 99% 9.69M 0s\n",
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      " 45800K .......... .......... .......... .......... .......... 99% 4.06M 0s\n",
      " 45850K .......... .......... .......... .......... .......... 99% 5.23M 0s\n",
      " 45900K .......... .......... .......... .......... .......... 99% 8.59M 0s\n",
      " 45950K .......... .......... .......... .......... .......... 99% 12.5M 0s\n",
      " 46000K .......... .......... .......... .......... .......... 99% 11.6M 0s\n",
      " 46050K .......... .......... .......... .......... .......... 99% 7.27M 0s\n",
      " 46100K .......... .......... .......... .......... .......... 99% 14.3M 0s\n",
      " 46150K .......... .......... ........                        100% 9.20M=8.0s\n",
      "\n",
      "2021-08-14 14:31:22 (5.61 MB/s) - 'hymenoptera_data.zip' saved [47286322/47286322]\n",
      "\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "source": [
    "import os\r\n",
    "from pathlib import Path\r\n",
    "\r\n",
    "print('WD:',os.getcwd())\r\n",
    "# os.chdir()\r\n",
    "DATA_PATH = Path(r'C:\\files\\git_repository\\pytorch-learning\\datasets\\hymenoptera_data')\r\n",
    "print('DATA_PATH:',DATA_PATH)"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "WD: c:\\files\\git_repository\\pytorch-learning\\2pytorch基础入门实战\\13基于迁移学习的蚁蜂分类模型\n",
      "DATA_PATH: C:\\files\\git_repository\\pytorch-learning\\datasets\\hymenoptera_data\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "接下来，让我们查看一下 该数据集的结构："
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "# !ls  hymenoptera_data\r\n",
    "# !ls  hymenoptera_data/train\r\n",
    "# !ls  hymenoptera_data/val"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "可以看到该数据集已经将训练数据和测试数据分开，且它们里面都存在着两个类别的数据：蜜蜂图像和蚂蚁图像。"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "具体图像如下所示："
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "<img src=\"https://doc.shiyanlou.com/courses/2534/1166617/18f1acac22d63bbb8d1c141de3c965fe-0/wm\">"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "我们的目的就是建立一个良好的模型，使其能够判断任意一张图像是蜜蜂还是蚂蚁。"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### 数据预处理操作"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "在加载数据之前，让我们还是先来定义数据预处理的操作集合。我们可以使用旋转、剪切等操作，扩大数据集合的多样性。代码如下："
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "source": [
    "import torch\r\n",
    "import torchvision\r\n",
    "import numpy as np\r\n",
    "from torchvision import datasets, transforms\r\n",
    "# 用于存在标准化，因此这里定义了高斯分布所需要的两个参数：均值和标准差\r\n",
    "mean = np.array([0.5, 0.5, 0.5])\r\n",
    "std = np.array([0.25, 0.25, 0.25])\r\n",
    "\r\n",
    "\r\n",
    "# data_transforms可以以字典的方式传入以达到对不同文件夹进行不同的处理\r\n",
    "# key文件夹名字，value为transforms的compose列表\r\n",
    "data_transforms = {\r\n",
    "\r\n",
    "    'train': transforms.Compose([\r\n",
    "        # 加入旋转等数据增强技术，加大模型训练的难度，提高模型的稳健性\r\n",
    "        transforms.RandomResizedCrop(224),\r\n",
    "        transforms.RandomHorizontalFlip(),\r\n",
    "        transforms.ToTensor(),\r\n",
    "        transforms.Normalize(mean, std)\r\n",
    "    ]),\r\n",
    "    # 测试集输入时，无需加入旋转等操作\r\n",
    "    'val': transforms.Compose([\r\n",
    "        # resize 操作的目的是将任意大小的图片转为模型规定的输入大小\r\n",
    "        transforms.Resize(256),\r\n",
    "        transforms.CenterCrop(224),\r\n",
    "        transforms.ToTensor(),\r\n",
    "        transforms.Normalize(mean, std)\r\n",
    "    ]),\r\n",
    "}\r\n",
    "data_transforms"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "{'train': Compose(\n",
       "     RandomResizedCrop(size=(224, 224), scale=(0.08, 1.0), ratio=(0.75, 1.3333), interpolation=bilinear)\n",
       "     RandomHorizontalFlip(p=0.5)\n",
       "     ToTensor()\n",
       "     Normalize(mean=[0.5 0.5 0.5], std=[0.25 0.25 0.25])\n",
       " ),\n",
       " 'val': Compose(\n",
       "     Resize(size=256, interpolation=bilinear, max_size=None, antialias=None)\n",
       "     CenterCrop(size=(224, 224))\n",
       "     ToTensor()\n",
       "     Normalize(mean=[0.5 0.5 0.5], std=[0.25 0.25 0.25])\n",
       " )}"
      ]
     },
     "metadata": {},
     "execution_count": 2
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### 数据的加载"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "接下来，就让我们加载数据集合，并且将其制作成 PyTorch 能够识别的数据加载器："
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "source": [
    "import os\r\n",
    "\r\n",
    "data_dir = r'C:\\files\\git_repository\\pytorch-learning\\datasets\\hymenoptera_data'\r\n",
    "# 将数据集封装到 PyTorch 中数据加载器中\r\n",
    "image_datasets = {x: datasets.ImageFolder(os.path.join(data_dir, x),\r\n",
    "                                          data_transforms[x])\r\n",
    "                  for x in ['train', 'val']}\r\n",
    "dataloaders = {x: torch.utils.data.DataLoader(image_datasets[x], batch_size=4,\r\n",
    "                                              shuffle=True, num_workers=0)\r\n",
    "               for x in ['train', 'val']}\r\n",
    "dataset_sizes = {x: len(image_datasets[x]) for x in ['train', 'val']}\r\n",
    "class_names = image_datasets['train'].classes\r\n",
    "# 判断当前环境\r\n",
    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\r\n",
    "\r\n",
    "print(class_names)"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "['ants', 'bees']\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "source": [
    "\"\"\" 生成数据集  \"\"\"\r\n",
    "from torch.utils.data import DataLoader\r\n",
    "from torchvision.datasets import  ImageFolder\r\n",
    "\r\n",
    "\r\n",
    "train_floder = ImageFolder(root = str(DATA_PATH/'train'),transform=data_transforms['train'])\r\n",
    "test_floder = ImageFolder(root = str(DATA_PATH/'val'),transform = data_transforms['val'])\r\n",
    "\r\n",
    "train_loader = DataLoader(train_floder,batch_size=32)\r\n",
    "test_loader = DataLoader(test_floder,batch_size=64)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### 数据可视化"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "接下来，我们还是利用定义好的数据加载器随机展示几张数据集中的图像："
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "source": [
    "import matplotlib.pyplot as plt\r\n",
    "%matplotlib inline\r\n",
    "\r\n",
    "\r\n",
    "def imshow(inp, title):\r\n",
    "    \"\"\"Imshow for Tensor.\"\"\"\r\n",
    "    inp = inp.numpy().transpose((1, 2, 0))\r\n",
    "    inp = std * inp + mean\r\n",
    "    inp = np.clip(inp, 0, 1)\r\n",
    "    plt.imshow(inp)\r\n",
    "    plt.title(title)\r\n",
    "    plt.show()\r\n",
    "\r\n",
    "\r\n",
    "# 获得训练数据中的一个批次的数据\r\n",
    "inputs, classes = next(iter(dataloaders['train']))\r\n",
    "\r\n",
    "# 将图片拼成一个网格\r\n",
    "out = torchvision.utils.make_grid(inputs)\r\n",
    "# 展示图像\r\n",
    "imshow(out, title=[class_names[x] for x in classes])"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ],
      "image/svg+xml": "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\r\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\r\n  \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\r\n<svg height=\"130.450555pt\" version=\"1.1\" viewBox=\"0 0 375.2875 130.450555\" width=\"375.2875pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\r\n <metadata>\r\n  <rdf:RDF xmlns:cc=\"http://creativecommons.org/ns#\" xmlns:dc=\"http://purl.org/dc/elements/1.1/\" xmlns:rdf=\"http://www.w3.org/1999/02/22-rdf-syntax-ns#\">\r\n   <cc:Work>\r\n    <dc:type rdf:resource=\"http://purl.org/dc/dcmitype/StillImage\"/>\r\n    <dc:date>2021-08-14T14:47:30.980990</dc:date>\r\n    <dc:format>image/svg+xml</dc:format>\r\n    <dc:creator>\r\n     <cc:Agent>\r\n      <dc:title>Matplotlib v3.4.2, https://matplotlib.org/</dc:title>\r\n     </cc:Agent>\r\n    </dc:creator>\r\n   </cc:Work>\r\n  </rdf:RDF>\r\n </metadata>\r\n <defs>\r\n  <style 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"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "source": [
    "class_dict = train_floder.class_to_idx\r\n",
    "class_dict = dict(zip(class_dict.values(),class_dict.keys()))\r\n",
    "print(class_dict)"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "{0: 'ants', 1: 'bees'}\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "source": [
    "\"\"\" 查看图像 \"\"\"\r\n",
    "import matplotlib.pyplot as plt\r\n",
    "\r\n",
    "\r\n",
    "x,y = next(iter(train_loader))\r\n",
    "# x,y = next(iter(test_loader))\r\n",
    "\r\n",
    "for i in range(4):\r\n",
    "    img = x[i].permute(1,2,0)\r\n",
    "    name_idx = y[i].item()\r\n",
    "    name =  class_dict[name_idx]\r\n",
    "    print(img.shape)\r\n",
    "    print(name)\r\n",
    "    plt.subplot(1,4,i+1).set_title(name)\r\n",
    "    plt.imshow(img)"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "torch.Size([224, 224, 3])\n",
      "ants\n",
      "torch.Size([224, 224, 3])\n",
      "ants\n",
      "torch.Size([224, 224, 3])\n",
      "ants\n",
      "torch.Size([224, 224, 3])\n",
      "ants\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ],
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"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 模型的建立"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### 迁移学习"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "迁移学习的核心思想其实就是将一个问题的训练模型应用到其他问题之中。当然这种转移并不是完完全全的复制，在迁移的过程中我们也需要调节模型的参数。简单的说，就是一种迁移学习举一反三的学习能力。比如我们学会其自行车后，学习骑摩托车就会很简单。"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "举个例子，如果我们这里已经存在一个模型，该模型是用于猫狗分类的一个模型，模型准确率能够达到 80%。而此时我们需要解决的问题是蚂蚁和蜜蜂的分类问题，我们可以重新建立一个模型进行训练。我们也可以将猫狗分类模型作为蜂蚁分类模型的初始模型，进行训练。这个将一个问题的已训练模型加载到另一个问题之中，作为初始模型的过程叫做迁移学习。"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "传统的模型训练就是建立一个神经网络结构，给该网络结构的参数进行随机取值。然后将数据集放入模型中进行训练，在训练过程中优化模型参数的过程。如下所示："
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "<img width=\"600px\" src=\"https://doc.shiyanlou.com/courses/2534/1166617/f3a236c6897175a6f914cb9008b5ab0e-0/wm\">"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "同样的网络结构下，迁移学习就是在模型训练之前，先给该网络结构赋予初值，然后再进行模型的训练，优化模型参数。"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "那么这些初值是哪里来的呢？这些初值可以来自与本任务无关的其他任务的训练模型之中。如下图，蜂蚁分类模型的初始模型其实就是猫狗分类的已训练模型（这种已经训练好的模型，也被称之为预训练模型，即提前训练的模型。）："
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "<img width=\"400px\" src=\"https://doc.shiyanlou.com/courses/2534/1166617/aba974e4122f8e0c1e8c1055c7caabc8-0/wm\">"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "如上所示，如果在之前的学习中，我们已经训练了一个猫狗分类的模型。然后今天需要训练一个蚂蚁和蜜蜂的分类模型，由于都是二分类问题，我们就可以借用猫狗分类的神经网络结构以及它的模型参数。"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "当然，如果直接用猫狗分类器来分类蚂蚁和蜜蜂的话，那么准确率一样会很低。因此，我们还是需要进行模型的训练。"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "综上，迁移学习的训练和传统模型的训练的唯一不同就是，在模型初始化时的参数的取值方式不同。"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "从上面的结论可以看出，无论我们是否引入预训练模型，仿佛我们都需要进行模型的训练，那么我们引入预训练模型有什么好处呢？"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "其中的缘由非常复杂，这里直接告诉你一个结论：我们引入预训练模型，可以大大地提高模型的训练速度。即采用预训练模型中的参数作为训练的开始，比随机初始化模型参数的收敛速度快。"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    " #### 预训练模型的加载"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "PyTorch 已经将一些经典的网络结构和已训练模型进行了打包，并放到了 `torchvision.model` 之中。我们可以直接对其进行加载。"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "本实验我们将使用 resnet18 网络结构来对蚂蚁和蜜蜂进行分类，该结构的网络模型如下："
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "<img width=\"400px\" src=\"https://doc.shiyanlou.com/courses/2534/1166617/ec993cfb87cc866fd8823a61309ab6f5-0\">"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "我们可以像上一章节一样，手动实现该模型，也可以直接加载预训练模型："
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "source": [
    "from torchvision import models\r\n",
    "import torch\r\n",
    "\r\n",
    "# 指定预训练模型的网络地址，如果不指定，则会从 PyTorch 官网上下载\r\n",
    "# 由于官网属于外网，速度很慢。因此这里已将模型上传至云服务器中\r\n",
    "torch.utils.model_zoo.load_url(\r\n",
    "\"https://labfile.oss.aliyuncs.com/courses/2534/resnet18-5c106cde.pth\")\r\n",
    "# 加载预训练模型\r\n",
    "# pretrained=False 表示只加载结构，不加载模型\r\n",
    "# pretrained=True：表示都加载\r\n",
    "model = models.resnet18(pretrained=True)\r\n",
    "model"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "ResNet(\n",
       "  (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
       "  (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (relu): ReLU(inplace=True)\n",
       "  (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
       "  (layer1): Sequential(\n",
       "    (0): BasicBlock(\n",
       "      (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (1): BasicBlock(\n",
       "      (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "  )\n",
       "  (layer2): Sequential(\n",
       "    (0): BasicBlock(\n",
       "      (conv1): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (downsample): Sequential(\n",
       "        (0): Conv2d(64, 128, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "        (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "    )\n",
       "    (1): BasicBlock(\n",
       "      (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "  )\n",
       "  (layer3): Sequential(\n",
       "    (0): BasicBlock(\n",
       "      (conv1): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (downsample): Sequential(\n",
       "        (0): Conv2d(128, 256, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "    )\n",
       "    (1): BasicBlock(\n",
       "      (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "  )\n",
       "  (layer4): Sequential(\n",
       "    (0): BasicBlock(\n",
       "      (conv1): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (downsample): Sequential(\n",
       "        (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "        (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "    )\n",
       "    (1): BasicBlock(\n",
       "      (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "  )\n",
       "  (avgpool): AdaptiveAvgPool2d(output_size=(1, 1))\n",
       "  (fc): Linear(in_features=512, out_features=1000, bias=True)\n",
       ")"
      ]
     },
     "metadata": {},
     "execution_count": 7
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "那么，是否上面这个模型就可以识别蜜蜂和蚂蚁了呢？或许细心的你已经发现，该模型的输出为 1000 个节点，而我们对蚂蚁和蜜蜂进行分类，只需要两个输出节点。综上，我们需要对 fc 层进行修改，使其只输出 2 个神经元节点。代码如下："
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "source": [
    "import torch.nn as nn\r\n",
    "\r\n",
    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\r\n",
    "\r\n",
    "# 获得 fc 层的输入节点数\r\n",
    "num_ftrs = model.fc.in_features\r\n",
    "# 重新定义 fc：输入节点数不变的情况下，将输出节点改为 2\r\n",
    "model.fc = nn.Linear(num_ftrs, 2)\r\n",
    "model = model.to(device)\r\n",
    "model"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "ResNet(\n",
       "  (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
       "  (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (relu): ReLU(inplace=True)\n",
       "  (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
       "  (layer1): Sequential(\n",
       "    (0): BasicBlock(\n",
       "      (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (1): BasicBlock(\n",
       "      (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "  )\n",
       "  (layer2): Sequential(\n",
       "    (0): BasicBlock(\n",
       "      (conv1): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (downsample): Sequential(\n",
       "        (0): Conv2d(64, 128, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "        (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "    )\n",
       "    (1): BasicBlock(\n",
       "      (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "  )\n",
       "  (layer3): Sequential(\n",
       "    (0): BasicBlock(\n",
       "      (conv1): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (downsample): Sequential(\n",
       "        (0): Conv2d(128, 256, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "    )\n",
       "    (1): BasicBlock(\n",
       "      (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "  )\n",
       "  (layer4): Sequential(\n",
       "    (0): BasicBlock(\n",
       "      (conv1): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (downsample): Sequential(\n",
       "        (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "        (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "    )\n",
       "    (1): BasicBlock(\n",
       "      (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "  )\n",
       "  (avgpool): AdaptiveAvgPool2d(output_size=(1, 1))\n",
       "  (fc): Linear(in_features=512, out_features=2, bias=True)\n",
       ")"
      ]
     },
     "metadata": {},
     "execution_count": 8
    }
   ],
   "metadata": {
    "scrolled": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "source": [
    "\"\"\" 基于pytoch-lighting编写训练系统 \"\"\"\r\n",
    "import pytorch_lightning as pl\r\n",
    "import torch.nn as nn\r\n",
    "import torch.nn.functional as F\r\n",
    "import torch\r\n",
    "\r\n",
    "class LitModel(pl.LightningModule):\r\n",
    "    def __init__(self,model):\r\n",
    "        super(LitModel,self).__init__()\r\n",
    "        self.model = model\r\n",
    "    \r\n",
    "    def forward(self,x):\r\n",
    "        x = self.model(x)\r\n",
    "        return x\r\n",
    "    \r\n",
    "    def training_step(self,batch,batch_idx):\r\n",
    "        x,y =  batch\r\n",
    "        y_pred = self.forward(x)\r\n",
    "        # 记住，大写的是对象，一定要先实例化再调用\r\n",
    "        loss =  nn.CrossEntropyLoss()(y_pred,y)\r\n",
    "        self.log('train_loss',loss)\r\n",
    "        return loss\r\n",
    "\r\n",
    "    def test_step(self,batch,batch_idx):\r\n",
    "        x,y =  batch\r\n",
    "        x = x.view(x.size(0),-1)\r\n",
    "        y_pred = self.forward(x)\r\n",
    "        loss =  nn.CrossEntropyLoss()(y_pred,y)\r\n",
    "        self.log('test_loss',loss)\r\n",
    "\r\n",
    "    def configure_optimizers(self):\r\n",
    "        optimizer = torch.optim.SGD(params=self.parameters(),lr=0.001)\r\n",
    "        return optimizer\r\n",
    "mymodel = LitModel(model)\r\n",
    "\r\n",
    "# test_tensor = torch.randn(32, 3, 224, 224)\r\n",
    "# mymodel(test_tensor)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "source": [
    "trainer =  pl.Trainer(max_epochs = 20)\r\n",
    "\r\n",
    "trainer.fit(mymodel,train_dataloaders=train_loader)\r\n",
    "trainer.test(mymodel,test_dataloaders=test_loader)"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "GPU available: False, used: False\n",
      "TPU available: False, using: 0 TPU cores\n",
      "IPU available: False, using: 0 IPUs\n",
      "\n",
      "  | Name  | Type   | Params\n",
      "---------------------------------\n",
      "0 | model | ResNet | 11.2 M\n",
      "---------------------------------\n",
      "11.2 M    Trainable params\n",
      "0         Non-trainable params\n",
      "11.2 M    Total params\n",
      "44.710    Total estimated model params size (MB)\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Epoch 0:   0%|          | 0/8 [09:14<1:13:59, 554.97s/it]\n",
      "Epoch 0:   0%|          | 0/8 [16:47<2:14:17, 1007.15s/it]\n",
      "Epoch 19: 100%|██████████| 8/8 [00:20<00:00,  2.27s/it, loss=0.777, v_num=3]\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "C:\\Users\\22459\\Miniconda3\\envs\\pytorch\\lib\\site-packages\\pytorch_lightning\\trainer\\trainer.py:679: LightningDeprecationWarning: `trainer.test(test_dataloaders)` is deprecated in v1.4 and will be removed in v1.6. Use `trainer.test(dataloaders)` instead.\n",
      "  rank_zero_deprecation(\n",
      "C:\\Users\\22459\\Miniconda3\\envs\\pytorch\\lib\\site-packages\\pytorch_lightning\\trainer\\data_loading.py:105: UserWarning: The dataloader, test dataloader 0, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 8 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.\n",
      "  rank_zero_warn(\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Testing: 0it [00:00, ?it/s]"
     ]
    },
    {
     "output_type": "error",
     "ename": "RuntimeError",
     "evalue": "Expected 4-dimensional input for 4-dimensional weight [64, 3, 7, 7], but got 2-dimensional input of size [64, 150528] instead",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-24-c0b4cac5c234>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mtrainer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmymodel\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mtrain_dataloaders\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtrain_loader\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m \u001b[0mtrainer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtest\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmymodel\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mtest_dataloaders\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtest_loader\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\Miniconda3\\envs\\pytorch\\lib\\site-packages\\pytorch_lightning\\trainer\\trainer.py\u001b[0m in \u001b[0;36mtest\u001b[1;34m(self, model, dataloaders, ckpt_path, verbose, datamodule, test_dataloaders)\u001b[0m\n\u001b[0;32m    704\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    705\u001b[0m         \u001b[1;31m# run test\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 706\u001b[1;33m         \u001b[0mresults\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_run\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    707\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    708\u001b[0m         \u001b[1;32massert\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstate\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstopped\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Miniconda3\\envs\\pytorch\\lib\\site-packages\\pytorch_lightning\\trainer\\trainer.py\u001b[0m in \u001b[0;36m_run\u001b[1;34m(self, model)\u001b[0m\n\u001b[0;32m    916\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    917\u001b[0m         \u001b[1;31m# dispatch `start_training` or `start_evaluating` or `start_predicting`\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 918\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_dispatch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    919\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    920\u001b[0m         \u001b[1;31m# plugin will finalized fitting (e.g. ddp_spawn will load trained model)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Miniconda3\\envs\\pytorch\\lib\\site-packages\\pytorch_lightning\\trainer\\trainer.py\u001b[0m in \u001b[0;36m_dispatch\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    980\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_dispatch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    981\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mevaluating\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 982\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0maccelerator\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstart_evaluating\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    983\u001b[0m         \u001b[1;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredicting\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    984\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0maccelerator\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstart_predicting\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Miniconda3\\envs\\pytorch\\lib\\site-packages\\pytorch_lightning\\accelerators\\accelerator.py\u001b[0m in \u001b[0;36mstart_evaluating\u001b[1;34m(self, trainer)\u001b[0m\n\u001b[0;32m     93\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     94\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mstart_evaluating\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtrainer\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;34m\"pl.Trainer\"\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m->\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 95\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtraining_type_plugin\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstart_evaluating\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtrainer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     96\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     97\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mstart_predicting\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtrainer\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;34m\"pl.Trainer\"\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m->\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Miniconda3\\envs\\pytorch\\lib\\site-packages\\pytorch_lightning\\plugins\\training_type\\training_type_plugin.py\u001b[0m in \u001b[0;36mstart_evaluating\u001b[1;34m(self, trainer)\u001b[0m\n\u001b[0;32m    163\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mstart_evaluating\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtrainer\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;34m\"pl.Trainer\"\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m->\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    164\u001b[0m         \u001b[1;31m# double dispatch to initiate the test loop\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 165\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_results\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtrainer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrun_stage\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    166\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    167\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mstart_predicting\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtrainer\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;34m\"pl.Trainer\"\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m->\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Miniconda3\\envs\\pytorch\\lib\\site-packages\\pytorch_lightning\\trainer\\trainer.py\u001b[0m in \u001b[0;36mrun_stage\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    991\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    992\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mevaluating\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 993\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_run_evaluate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    994\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredicting\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    995\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_run_predict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Miniconda3\\envs\\pytorch\\lib\\site-packages\\pytorch_lightning\\trainer\\trainer.py\u001b[0m in \u001b[0;36m_run_evaluate\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m   1077\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1078\u001b[0m         \u001b[1;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprofiler\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprofile\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mf\"run_{self.state.stage}_evaluation\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtorch\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mno_grad\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1079\u001b[1;33m             \u001b[0meval_loop_results\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_evaluation_loop\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1080\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1081\u001b[0m         \u001b[1;31m# remove the tensors from the eval results\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Miniconda3\\envs\\pytorch\\lib\\site-packages\\pytorch_lightning\\loops\\base.py\u001b[0m in \u001b[0;36mrun\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m    109\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    110\u001b[0m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mon_advance_start\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 111\u001b[1;33m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madvance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    112\u001b[0m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mon_advance_end\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    113\u001b[0m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0miteration_count\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Miniconda3\\envs\\pytorch\\lib\\site-packages\\pytorch_lightning\\loops\\dataloader\\evaluation_loop.py\u001b[0m in \u001b[0;36madvance\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m    108\u001b[0m         \u001b[0mdl_max_batches\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_max_batches\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcurrent_dataloader_idx\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    109\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 110\u001b[1;33m         dl_outputs = self.epoch_loop.run(\n\u001b[0m\u001b[0;32m    111\u001b[0m             \u001b[0mdataloader_iter\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcurrent_dataloader_idx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdl_max_batches\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnum_dataloaders\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    112\u001b[0m         )\n",
      "\u001b[1;32m~\\Miniconda3\\envs\\pytorch\\lib\\site-packages\\pytorch_lightning\\loops\\base.py\u001b[0m in \u001b[0;36mrun\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m    109\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    110\u001b[0m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mon_advance_start\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 111\u001b[1;33m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madvance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    112\u001b[0m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mon_advance_end\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    113\u001b[0m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0miteration_count\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Miniconda3\\envs\\pytorch\\lib\\site-packages\\pytorch_lightning\\loops\\epoch\\evaluation_epoch_loop.py\u001b[0m in \u001b[0;36madvance\u001b[1;34m(self, dataloader_iter, dataloader_idx, dl_max_batches, num_dataloaders)\u001b[0m\n\u001b[0;32m    108\u001b[0m         \u001b[1;31m# lightning module methods\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    109\u001b[0m         \u001b[1;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtrainer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprofiler\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprofile\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"evaluation_step_and_end\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 110\u001b[1;33m             \u001b[0moutput\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mevaluation_step\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbatch_idx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdataloader_idx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    111\u001b[0m             \u001b[0moutput\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mevaluation_step_end\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0moutput\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    112\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Miniconda3\\envs\\pytorch\\lib\\site-packages\\pytorch_lightning\\loops\\epoch\\evaluation_epoch_loop.py\u001b[0m in \u001b[0;36mevaluation_step\u001b[1;34m(self, batch, batch_idx, dataloader_idx)\u001b[0m\n\u001b[0;32m    148\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtrainer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlightning_module\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_current_fx_name\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m\"test_step\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    149\u001b[0m             \u001b[1;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtrainer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprofiler\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprofile\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"test_step\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 150\u001b[1;33m                 \u001b[0moutput\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtrainer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0maccelerator\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtest_step\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstep_kwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    151\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    152\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtrainer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlightning_module\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_current_fx_name\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m\"validation_step\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Miniconda3\\envs\\pytorch\\lib\\site-packages\\pytorch_lightning\\accelerators\\accelerator.py\u001b[0m in \u001b[0;36mtest_step\u001b[1;34m(self, step_kwargs)\u001b[0m\n\u001b[0;32m    224\u001b[0m         \"\"\"\n\u001b[0;32m    225\u001b[0m         \u001b[1;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprecision_plugin\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtest_step_context\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtraining_type_plugin\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtest_step_context\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 226\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtraining_type_plugin\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtest_step\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mstep_kwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    227\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    228\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mpredict_step\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstep_kwargs\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mDict\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mstr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mUnion\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mAny\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mint\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m->\u001b[0m \u001b[0mSTEP_OUTPUT\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Miniconda3\\envs\\pytorch\\lib\\site-packages\\pytorch_lightning\\plugins\\training_type\\training_type_plugin.py\u001b[0m in \u001b[0;36mtest_step\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m    179\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    180\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mtest_step\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 181\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtest_step\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    182\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    183\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mpredict_step\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<ipython-input-23-240b1699b1b6>\u001b[0m in \u001b[0;36mtest_step\u001b[1;34m(self, batch, batch_idx)\u001b[0m\n\u001b[0;32m     24\u001b[0m         \u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m  \u001b[0mbatch\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     25\u001b[0m         \u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mview\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msize\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 26\u001b[1;33m         \u001b[0my_pred\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     27\u001b[0m         \u001b[0mloss\u001b[0m \u001b[1;33m=\u001b[0m  \u001b[0mnn\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mCrossEntropyLoss\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my_pred\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     28\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlog\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'test_loss'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mloss\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<ipython-input-23-240b1699b1b6>\u001b[0m in \u001b[0;36mforward\u001b[1;34m(self, x)\u001b[0m\n\u001b[0;32m     11\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     12\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 13\u001b[1;33m         \u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     14\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     15\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Miniconda3\\envs\\pytorch\\lib\\site-packages\\torch\\nn\\modules\\module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[1;34m(self, *input, **kwargs)\u001b[0m\n\u001b[0;32m   1049\u001b[0m         if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks\n\u001b[0;32m   1050\u001b[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[1;32m-> 1051\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1052\u001b[0m         \u001b[1;31m# Do not call functions when jit is used\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1053\u001b[0m         \u001b[0mfull_backward_hooks\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnon_full_backward_hooks\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Miniconda3\\envs\\pytorch\\lib\\site-packages\\torchvision\\models\\resnet.py\u001b[0m in \u001b[0;36mforward\u001b[1;34m(self, x)\u001b[0m\n\u001b[0;32m    247\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    248\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mTensor\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m->\u001b[0m \u001b[0mTensor\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 249\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_forward_impl\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    250\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    251\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Miniconda3\\envs\\pytorch\\lib\\site-packages\\torchvision\\models\\resnet.py\u001b[0m in \u001b[0;36m_forward_impl\u001b[1;34m(self, x)\u001b[0m\n\u001b[0;32m    230\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_forward_impl\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mTensor\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m->\u001b[0m \u001b[0mTensor\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    231\u001b[0m         \u001b[1;31m# See note [TorchScript super()]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 232\u001b[1;33m         \u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconv1\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    233\u001b[0m         \u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbn1\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    234\u001b[0m         \u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrelu\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Miniconda3\\envs\\pytorch\\lib\\site-packages\\torch\\nn\\modules\\module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[1;34m(self, *input, **kwargs)\u001b[0m\n\u001b[0;32m   1049\u001b[0m         if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks\n\u001b[0;32m   1050\u001b[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[1;32m-> 1051\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1052\u001b[0m         \u001b[1;31m# Do not call functions when jit is used\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1053\u001b[0m         \u001b[0mfull_backward_hooks\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnon_full_backward_hooks\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Miniconda3\\envs\\pytorch\\lib\\site-packages\\torch\\nn\\modules\\conv.py\u001b[0m in \u001b[0;36mforward\u001b[1;34m(self, input)\u001b[0m\n\u001b[0;32m    441\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    442\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minput\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mTensor\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m->\u001b[0m \u001b[0mTensor\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 443\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_conv_forward\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mweight\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbias\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    444\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    445\u001b[0m \u001b[1;32mclass\u001b[0m \u001b[0mConv3d\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0m_ConvNd\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Miniconda3\\envs\\pytorch\\lib\\site-packages\\torch\\nn\\modules\\conv.py\u001b[0m in \u001b[0;36m_conv_forward\u001b[1;34m(self, input, weight, bias)\u001b[0m\n\u001b[0;32m    437\u001b[0m                             \u001b[0mweight\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbias\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstride\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    438\u001b[0m                             _pair(0), self.dilation, self.groups)\n\u001b[1;32m--> 439\u001b[1;33m         return F.conv2d(input, weight, bias, self.stride,\n\u001b[0m\u001b[0;32m    440\u001b[0m                         self.padding, self.dilation, self.groups)\n\u001b[0;32m    441\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mRuntimeError\u001b[0m: Expected 4-dimensional input for 4-dimensional weight [64, 3, 7, 7], but got 2-dimensional input of size [64, 150528] instead"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 模型的训练与测试"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### 损失和优化器的定义"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "这里我们还是采用常用的交叉熵损失作为模型训练所需的损失函数，使用 SGD 优化器来对模型进行优化求解。"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "source": [
    "import torch.optim as optim\r\n",
    "\r\n",
    "criterion = nn.CrossEntropyLoss()\r\n",
    "optimizer = optim.SGD(model.parameters(), lr=0.001)\r\n",
    "criterion, optimizer"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "(CrossEntropyLoss(),\n",
       " SGD (\n",
       " Parameter Group 0\n",
       "     dampening: 0\n",
       "     lr: 0.001\n",
       "     momentum: 0\n",
       "     nesterov: False\n",
       "     weight_decay: 0\n",
       " ))"
      ]
     },
     "metadata": {},
     "execution_count": 22
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "在定义完优化器后，我们还可以定义一个学习率的变化器。传统梯度下降算法的学习率是不变的，我们可以利用 lr_scheduler 定义一个随着 epoch 变化的学习率。这样可以更好的优化模型，提高模型的准确率和收敛速度。"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "source": [
    "from torch.optim import lr_scheduler\r\n",
    "\r\n",
    "step_lr_scheduler = lr_scheduler.StepLR(optimizer, step_size=7, gamma=0.1)\r\n",
    "step_lr_scheduler"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<torch.optim.lr_scheduler.StepLR at 0x1221150a8b0>"
      ]
     },
     "metadata": {},
     "execution_count": 23
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### 训练与测试"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "在模型训练之前，让我们先来定义两个变量 `best_model_wts` 和 `best_acc` ，用于存储最佳模型参数和最高的模型准确率。"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "import copy\r\n",
    "# deepcopy：深拷贝，就是保存模型的参数复制到另一个变量中\r\n",
    "best_model_wts = copy.deepcopy(model.state_dict())\r\n",
    "best_acc = 0.0\r\n",
    "best_model_wts, best_acc"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "在后面的模型训练中，我们会不断更新这两个变量，使这两个变量在训练过程中，一直保存着的最佳模型的参数和测试准确率。"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "本次实验，我们将训练过程和测试过程放在了一个循环之中。"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "每一次的迭代中，我们都先对所有数据进行梯度下降算法，优化模型参数。然后再利用测试集得到优化后的模型准确率。如果该准确率比 `best_acc` 大，则将该模型的参数复制给 `best_model_wts` ，再进行下一次的迭代。反之，则不保存，直接进行下一次的迭代。"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "具体代码如下所示（下面训练代码可能会运行 6~8 min，请耐心等待）："
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "import time\r\n",
    "# 记录模型训练的开始时间\r\n",
    "since = time.time()\r\n",
    "# 开始训练,这里我们只设置迭代 5次\r\n",
    "num_epochs = 5\r\n",
    "for epoch in range(num_epochs):\r\n",
    "    print('Epoch {}/{}'.format(epoch, num_epochs - 1))\r\n",
    "    print('-' * 10)\r\n",
    "\r\n",
    "    # 每次迭代，都会对模型进行一次训练和一次测试\r\n",
    "    for phase in ['train', 'val']:\r\n",
    "        if phase == 'train':\r\n",
    "            model.train()  # 告诉模型，现在是对模型进行训练，即需要梯度下降\r\n",
    "        else:\r\n",
    "            model.eval()   # 告诉模型，现在是对模型进行测试，即不需要梯度下降\r\n",
    "\r\n",
    "        # 定义每次跌打时的损失和准确率\r\n",
    "        running_loss = 0.0\r\n",
    "        running_corrects = 0\r\n",
    "\r\n",
    "        # 遍历数据生成器，训练时 phase = train，测试时 phase = val\r\n",
    "        for inputs, labels in dataloaders[phase]:\r\n",
    "            inputs = inputs.to(device)\r\n",
    "            labels = labels.to(device)\r\n",
    "\r\n",
    "            # 前向传播\r\n",
    "            # 如果 phase == 'train'，则打开梯度。否则，关闭梯度\r\n",
    "            with torch.set_grad_enabled(phase == 'train'):\r\n",
    "                # 获得预测结果和损失\r\n",
    "                outputs = model(inputs)\r\n",
    "                _, preds = torch.max(outputs, 1)\r\n",
    "                loss = criterion(outputs, labels)\r\n",
    "\r\n",
    "                # 如果是模型训练，则需要反向传播\r\n",
    "                if phase == 'train':\r\n",
    "                    optimizer.zero_grad()\r\n",
    "                    loss.backward()\r\n",
    "                    optimizer.step()\r\n",
    "\r\n",
    "            # 统计损失值和正确的数据条数\r\n",
    "            running_loss += loss.item() * inputs.size(0)\r\n",
    "            running_corrects += torch.sum(preds == labels.data)\r\n",
    "        # 更新学习率\r\n",
    "        if phase == 'train':\r\n",
    "            step_lr_scheduler.step()\r\n",
    "\r\n",
    "        # 计算当前损失和准确率\r\n",
    "        epoch_loss = running_loss / dataset_sizes[phase]\r\n",
    "        epoch_acc = running_corrects.double() / dataset_sizes[phase]\r\n",
    "        print('{} Loss: {:.4f} Acc: {:.4f}'.format(\r\n",
    "            phase, epoch_loss, epoch_acc))\r\n",
    "\r\n",
    "        # 如果此时的测试准确率比 best_acc，那么就将此时的模型存入 best_model_wts 中\r\n",
    "        # 将准确率存入 best_acc 中\r\n",
    "        if phase == 'val' and epoch_acc > best_acc:\r\n",
    "            best_acc = epoch_acc\r\n",
    "            best_model_wts = copy.deepcopy(model.state_dict())\r\n",
    "\r\n",
    "    print(\"-----------------------\")\r\n",
    "\r\n",
    "# 模型训练完毕，计算花费时间\r\n",
    "time_elapsed = time.time() - since\r\n",
    "print('Training complete in {:.0f}m {:.0f}s'.format(\r\n",
    "    time_elapsed // 60, time_elapsed % 60))\r\n",
    "print('Best val Acc: {:4f}'.format(best_acc))\r\n",
    "\r\n",
    "# 加载训练过程中准确率最高的模型，作为输出的最后的模型\r\n",
    "model.load_state_dict(best_model_wts)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "我们只对上面模型进行了 5 次训练，就得到了 90% 以上的测试准确率。从结果可以看出，通过迁移学习设置模型的初始化参数，可以在很短的时间内得到准确率较高的蜂蚁分类模型。"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### 模型的应用"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "接下来，让我们利用建立的模型对任意一张图片进行识别。这里我从网上随机下载了一张蚂蚁的图片，让我们来验证一下，模型是否能够成功识别该图片。"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "首先将图片下载到环境中："
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "!wget -nc \"https://labfile.oss.aliyuncs.com/courses/2534/ant.jpg\""
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "接下来，让我们利用 PIL 加载该图像："
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "from PIL import Image\r\n",
    "\r\n",
    "# 可以通过修改下面路径，对需要预测的图片进行修改\r\n",
    "infer_path = 'ant.jpg'\r\n",
    "img = Image.open(infer_path)\r\n",
    "plt.imshow(img)\r\n",
    "plt.show()"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "从上图可以看到，该图像中存在很多的蚂蚁。在将图像放入模型中进行预测之前，我们需要对图片进行预处理，将图片处理成模型要求的输入格式："
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "def load_image(file):\r\n",
    "    im = Image.open(file)\r\n",
    "    # 将大小修改为 224*224 符合模型输入\r\n",
    "    im = im.resize((224, 224), Image.ANTIALIAS)\r\n",
    "    # 建立图片矩阵\r\n",
    "    im = np.array(im).astype(np.float32)\r\n",
    "    # WHC->CHW\r\n",
    "    im = im.transpose((2, 0, 1))\r\n",
    "    im = im / 255.0\r\n",
    "    # 转为 batch,c,w,h\r\n",
    "    im = np.expand_dims(im, axis=0)\r\n",
    "\r\n",
    "    print(\"im_shape 的维度\", im.shape)\r\n",
    "    return im\r\n",
    "\r\n",
    "\r\n",
    "# 测试函数\r\n",
    "img = load_image(infer_path)\r\n",
    "img"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "最后让我们将读出的影像放入模型之中，进行预测："
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "img_data = torch.tensor(img)\r\n",
    "outputs = model(img_data)\r\n",
    "_, preds = torch.max(outputs, 1)\r\n",
    "print(\"The picture is \", class_names[preds])"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "从结果可以看出，我们的模型成功预测出了网上随机找的一张影像的类别。你也可以在找几张蚂蚁和蜜蜂的图像进行测试，我想具有 90% 的识别准确率的模型一般能够识别大多数的蚂蚁和蜜蜂图像。"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "综上，我们通过迁移学习，在很短时间内，训练出了具有良好的泛化能力和鲁棒性的模型。"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 实验总结"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "本实验首先讲解了迁移学习的相关概念，然后利用 PyTorch 加载了 resnet18 的预训练模型，对模型的最后一层进行了修改，得到蜂蚁分类模型的初始模型。最后，我们只需要对模型进行短时间的训练，就可以得到准确率较高的蜂蚁分类模型。"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "<hr><div style=\"color: #999; font-size: 12px;\"><i class=\"fa fa-copyright\" aria-hidden=\"true\"> 本课程内容版权归蓝桥云课所有，禁止转载、下载及非法传播。</i></div>"
   ],
   "metadata": {}
  }
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